***** To join INSNA, visit http://www.insna.org ***** [Please accept our apologies if you received multiple copies of this call] CALL FOR PAPERS BigScholar 2019 The 6th Workshop on Big Scholarly Data https://urldefense.proofpoint.com/v2/url?u=http-3A__thealphalab.org_bigscholar_&d=DwIFJg&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=ei_eun7hM3K9OvoDjT3khvzQqmKnL4Xngm_5pEYzKBc&s=2IESycbITkw56xjf43pdQcfHpiEwEWynLYMvOG_twTw&e= A workshop of CIKM 2019 (The 28th ACM International Conference on Information and Knowledge Management) Beijing, China, November 3rd-7th, 2019 $B!!(B INTRODUCTION The number of scholarly documents produced by academics, researchers, and practitioners worldwide is increasing at an unprecedented speed. The term big scholarly data is coined for this rapidly growing scholarly source of information. Many large collections of scholarly data including digital libraries, search engines, repositories, knowledge bases, Wikipedia, and the Web have already covered millions of journal articles, conference proceedings, degree theses, books, patents, technical reports, tutorials, course materials, etc. For instance, the Microsoft Academic Graph contains scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study. The DBLP bibliography now lists more than 5000 conference and workshop series, as well as more than 1500 journals in computer science, which involve more than 4 million publications by more than 2 million authors. Big scholarly data bring about new opportunities and challenges with respect to knowledge discovery, data mining, science of science, and education. It is imperative and vital for scholars to drive their knowledge towards the innovative generation of values from big scholarly data. New knowledge can be extracted by analyzing and mining big scholarly data to, e.g., better understand research dynamics, scientific collaboration and success, identify new directions of research, assess the quality of science, and enable personalized teaching and learning. To achieve these goals, however, a lot of challenges facing big scholarly data acquisition, storage, management, processing and usage must be addressed. Following the success of the previous five editions, the BigScholar 2019 workshop aims at bringing together academics and practitioners from diverse fields to share ideas and experience with management, analysis, mining, and applications of big scholarly data. The goal is to contribute to the birth of a community having a shared interest around big scholarly data and exploring it using knowledge discovery, data science and analytics, network science, and other appropriate technologies. The workshop will be a half-day workshop. The format of the workshop will include keynote talks, research and position paper presentations, and one discussion panel. The workshop will be held in conjunction with the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019). Topics of interest include (but not limited to): - New approaches to search, crawling and integration of scholarly data from various data sources - Methods for storing, indexing, and query processing for big scholarly data - Practices for scholarly data management and sharing - Big scholarly data analysis, mining, and visualization - Network science for scholarly data analytics - Graph and text mining in big scholarly data - Measuring the impact of publications, funding, courses, individuals, teams, etc. - Computational behavioural sciences in research and education - Academic social network analysis and mining - Scholarly recommendation - Understanding and predicting success in research and education - Design of next generation platforms and systems for research and education - Novel services and applications for research and education IMPORTANT DATES: Submissions due: August 31, 2019 (Any time zone) SUBMISSION INSTRUCTIONS: Authors are invited to submit original papers that must not have been submitted to or published in any other workshop, conference, or journal. The workshop will accept full papers describing completed work, work-in-progress papers with preliminary results, as well as position papers reporting inspiring and intriguing new ideas. At least one author of each accepted papers must present their work at the workshop. All accepted papers will be published in the journal Frontiers in Big Data (as Article Collection on Big Scholarly Data, see: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.frontiersin.org_research-2Dtopics_8869_article-2Dcollection-2Don-2Dbig-2Dscholarly-2Ddata&d=DwIFJg&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=ei_eun7hM3K9OvoDjT3khvzQqmKnL4Xngm_5pEYzKBc&s=Bhym6qpEccg6bQSu-4SmkU2aO18MOYW2TiKrUc7w2Z8&e= ). All papers should be submitted via the Frontiers in Big Data journal submission system. Please go to: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.frontiersin.org_research-2Dtopics_8869_article-2Dcollection-2Don-2Dbig-2Dscholarly-2Ddata&d=DwIFJg&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=ei_eun7hM3K9OvoDjT3khvzQqmKnL4Xngm_5pEYzKBc&s=Bhym6qpEccg6bQSu-4SmkU2aO18MOYW2TiKrUc7w2Z8&e= (Click "Submit your manuscript") See also the Submission Instructions of the journal. All authors are encouraged to apply for full (or partial) waivers of Publishing Fees of the journal (not conference registeration). The journal has agreed to approve as many waiver applications from BigScholar 2019 authors as possible. For details on publishing fees of the journal, please visit: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.frontiersin.org_about_publishing-2Dfees&d=DwIFJg&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=ei_eun7hM3K9OvoDjT3khvzQqmKnL4Xngm_5pEYzKBc&s=ljJMGYypNlTMe4urFcuVKQN3ACDpaOdfELUItc46WVo&e= (see "Frontiers fee-waiver program" for how to apply for waivers). Organizers: Feng Xia, Dalian University of Technology Huan Liu, Arizona State University Irwin King, The Chinese University of Hong Kong Kuansan Wang, Microsoft Research Contact Info: Email: [log in to unmask] _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.